RFclust : A Toolkit for Random Forest Cluster Analysis (v0.1.4)
Bibliographic record
Abstract
Thirdc version of R package that combines random forest proximity matrices with consensus clustering for multiple data types. HOTFIX. Major update - I replaced the functions that compute random forest proximity matrices from Rcpp-based implementations in the package ranger.While that function does not handle unsupervised learning out of the box, I integrated custom code developed by the author of the ranger packageinto my workflow. Please always cite ranger as well as this package. Manuscript for RFclust is currently unpublished. Patch notes - code optimisation to mitigate memory use during proximity matrix generation. Bugfix to code used for single platform clustering with associated changed data specification. Now single platform data can be supplied as a matrix instead of in a named list. This also enables users to supply precompiled multiplatform data matrices instead of in a named list if more convenient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.130 | 0.174 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".